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Record W2111817266 · doi:10.3109/09638288.2010.515703

Partners towards autonomy: risky choices and relational autonomy in rehabilitation care

2010· article· en· W2111817266 on OpenAlexafffund
Matthew Hunt, Carolyn Ells

Bibliographic record

VenueDisability and Rehabilitation · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsAutonomyDeliberationPsychologyPerspective (graphical)RehabilitationGeneral partnershipHealth careSocial psychologyNursingMedicinePolitical science

Abstract

fetched live from OpenAlex

PURPOSE: A common source of ethical challenge for health care professionals (HCPs) in rehabilitation is situations when patients wish to make risky choices not related or contrary to rehabilitation goals. We explore the potential contribution of a relational understanding of autonomy for orienting clinical practice when patients wish to enact choices with associated risks for the patient or others. METHOD: We provide a theoretical analysis that is oriented by an examination of risk and a relational conception of autonomy, as relevant to rehabilitation care. We illustrate our analysis through the examination of a clinical case. RESULTS: Relational autonomy assumes that the patient, and the patient's decisional autonomy, is situated and shaped by relationships. From this perspective, HCPs can engage in a process of communication and deliberation with the patient about the risky choices at issue, leading towards improved patient autonomy. CONCLUSIONS: Relational autonomy can contribute to understanding patients' risky choices and guiding HCPs as they partner with patients towards autonomy. Such an approach supports patient-centred rehabilitation care. Ultimately, as clinicians respond to a patient who wishes to enact a risky choice, they should aim for a partnership towards autonomy with the patient and family.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.415
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2010
Admission routes2
Has abstractyes

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